2016_Detection of interturn faults.pdf (original) (raw)

Power transformers are designed to transmit and distribute electrical power. Depending on the size of a transformer, replacement costs can range from a few hundred dollars to millions of dollars. Performing invasive tests also add to the replacement cost. Hence, there is an increasing need to move from traditional schedule-based maintenance programs to condition-based maintenance. A focused approach is required for diagnostics. Considering the long service life of a power transformer and prevalent use of human judgment (expert), there is a need to develop a knowledge base expert system. This paper proposes a noninvasive approach, using digital signal processing wavelet-based artificial neural network technique for monitoring non stationary variations in order to distinguish between transformer inrush currents and transformer inter-turn faults. The performance of this algorithm is demonstrated on custom-built three-phase transform. Keywords— Transformer, Inrush, Interturn fault, Wave...

Transformers have become an important and efficient element of the power system. A fault or damage in the transformer windings will result in outage and heavy loss, as a result it needs to be monitored continuously. Due to the adaptation of various advanced techniques in monitoring and diagnostics of power system it has contributed a major hand in the research. The most common fault in the transformer (or in any electrical machine in general), is turn to turn fault in windings of transformer, and it is a type of incipient fault which cannot be detected easily as the magnitude of fault current is very nominal. This paper presents an approach to simulated model of turn to turn fault and its detection in a transformer by analyzing the neutral current of transformer after subjecting it to a standard impulse wave and subsequently applying wavelet transform. The identification of faults by using frequency or time domain based method is difficult. The Wavelet transform is most suitable met...

https://www.ijert.org/application-of-signal-analysis-for-fault-diagnosis-in-transformer-by-discrete-wavelet-transform https://www.ijert.org/research/application-of-signal-analysis-for-fault-diagnosis-in-transformer-by-discrete-wavelet-transform-IJERTV2IS2333.pdf Traditional protection gives only terminal condition on the basis of protection of transformer. Discrimination between an internal fault and a magnetizing inrush current has long been recognized as a challenge for power transformer protection. To characterize and discriminate the transient arising from magnetization and inter-turn faults are presented here. This characterization will give value added information for improving protection algorithm.. The detection method can provide information to predict fault ahead in time so as that necessary corrective actions are taken to prevent outages and reduce down time. The data is taken from different test results like normal (magnetization) and abnormal (inter-turn fault) in this work, Discrete Wavelet Transform concept is used. Feature extraction and method of discrimination between transformer magnetization and fault current is derived by Discrete Wavelet Transform (DWT) Tests are performed on 2KVA, 230/230Volt custom built single phase transformer. The results are found using Discrete and conclusion presented. Index Terms-Inrush current, internal fault, second harmonic component power transformer, wavelet transform.

This paper presents a technique for detecting and locating transformer inter-turn faults. The technique uses transformer secondary voltages and currents as inputs. It employs continuous wavelet transform (CWT) for input data processing and a trained convolutional neural network (CNN) as a decision tool. The processing of input data using the CWT results in six scalogram images. The six scalogram images are normalized and concatenated into a single image. The concatenated image is then fed into a trained CNN which indicates the occurrence or otherwise of an inter-turn fault. When an inter-turn fault is detected, the magnitude/severity of the fault (defined by the percentage turns affected) as well as the affected phase is determined. The technique was tested using simulations carried out on a 630kVA, 10.5kV/0.4kV, three-phase transformer which was modelled using MATLAB-Simulink. Test results show that the technique accurately detects and locates inter-turn faults. Furthermore, it can be integrated into existing numerical relays, for enhanced protection of transformers.

Interturn fault detection is a challenging issue in power transformer protection. In this paper, interturn faults of distribution transformer are studied and a new online detection method based on vibration analysis is proposed. Transformer electromagnetic forces are analyzed by time stepping finite element (TSFE) modeling of interturn fault. Since the vibration associated with inter-turn faults is caused by electromagnetic forces, axial and radial electromagnetic forces for various interturn faults are studied. Transformer winding vibration under interturn faults is studied through an equivalent mathematical model combined with electromagnetic force analysis. The results show that it is feasible to predict the interturn winding faults of transformer windings with the transformer vibration analysis method. Simulation and experimentation studies are carried out on 20/0.4 kV, 50 kVA distribution transformer. The results confirm the effectiveness of the proposed method.